Mining social network for extracting topic of textual conversations
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
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Effective search and retrieval are fundamental for realizing the full potential of the Web. Although nowadays search engines perform much better than few years ago, big improvements are still needed with respect to the relevance of the retrieved documents to the user's query and the presentation of the results. In this paper we present the prototype of a News retrieval system which exploits Wordnet's semantics in identifying the topic of retrieved documents and ranking them according to their relevance to the query. Also the system provides a short summary of each document, helping the user in browsing the result collection.